This week I received an interesting query, asking if I had any market sales quota data that could be used to assess if quotas were reasonable or not.
I found this a particularly interesting query. Firstly because I was curious to understand why they felt that market data would help them assess the effectiveness of quotas, and secondly because quota-setting, and everything connected to it, is perhaps the most
exasperating infuriating impossible delightfully intriguing area of sales incentives.
It is my belief that a request for sales plan market data is really the shadow of a deeper problem within the sales organisation. Don’t get me wrong, market data has its uses and is often fundamental to making big changes in a sales organisation but quota data in particular, is a dangerous thing to rely on.
Why? Firstly almost every company will claim to have difficulties around quota setting and any market data would reflect that. But dig a bit deeper and each company will have different job architectures, sales channel configuration, sales force structures (e.g. inside and pre-sales that may bear quota/overlay quota responsibility), stretch amounts, and sales strategies. Companies will also have different views on what the right distribution of “above target” performers is. 50%? 80%? But in my view, the biggest influencer is the vast number of different approaches to quota setting which can be applied.
So at this point you may well be asking, “well, braniac, if you can’t use market data then how do I get my quotas right?”
And a very wise question if I may say so.
Before reviewing and assessing the effectiveness of quotas, it is crucial to select the right approach for the sales organisation.
Choosing a quota-setting approach
The bad news is there are probably as many approaches to quota-setting as there are sales plans. They range from the very simple “base + whatever-the-VP-tells-us” approach, all the way through to more advanced approaches that rely on market data and regression analysis such as a “weighted index” approach, or the “performance frontier” method. Add to this rock star plans such as the particularly fascinating Gonik plan developed by Jacob Gonik for IBM, which also rewards on the accuracy of quotas that the sales reps themselves set, and there is an intimidatingly vast smorgasbord of options.
The correct approach may not only vary between companies but may vary between territories, and even products, and the final decision will depend on, amongst others:
- Sales strategy
- Market knowledge
- The availability and accuracy of market data
- The availability and accuracy of internal data
- Sophistication of analytical resources
- Quality of communication to sales force
- The ability of the sales force to understand such methods
- Personal preference
It is important to note that, unless you are extraordinarily jammy, the sum of the territory goals will not add up to the financial goals meaning the quotas will require adjustment.
Review and adapt
With a robust approach to quota-setting agreed upon, we come to the stage of evaluating how reasonable those quotas are. I’m a fan of scorecards and I don’t care what people think.
A scorecard approach will provide visibility to all stakeholders on how effectively we, as a company, are managing our quotas (the scorecard can also cover other areas of sales plan governance but this piece is getting on a bit so I will ignore that for now).
Distribution analysis is a straightforward and fundamental step in analysing any quota-setting process. Each company will have its own view on the appropriate distribution around key points such as threshold, target, and excellence point so it is difficult to choose a right or wrong answer. What is important is that once it is decided, we use this distribution to assess the accuracy of quotas. Reviewing the actual distribution against the normal distribution, by department, by product, by performance period can give valuable insight and assurance as to the accuracy of quotas.
There are also many forms of external analysis which will often naturally stem from the data used in the quota-setting process. If market penetration is used, for example, how do changes in this correlate to quota achievement? A period-by-period R² for performance against market penetration provides a high degree of assurance that achievement of quota correlates to market penetration.
Selecting a group of measures that can be reported on a regular basis provides a one-page analysis of the quota-setting process. Such data can convince even the most sceptical of sales leads that the process is robust and there you have it: no more quibbles with quotas.